CN109271594B - Recommendation method of electronic book, electronic equipment and computer storage medium - Google Patents
Recommendation method of electronic book, electronic equipment and computer storage medium Download PDFInfo
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Abstract
The invention discloses a recommendation method of an electronic book, electronic equipment and a computer storage medium, wherein the method comprises the following steps: acquiring user portrait data of a reading user; screening a golden sentence set matched with the reading user from a preset golden sentence database according to the user portrait data of the reading user and a preset golden sentence screening rule; and displaying the screened golden sentence set in a preset golden sentence list page so as to recommend the electronic book corresponding to the golden sentence set to the reading user through the golden sentence list page. Therefore, the method can recommend the personalized gold sentence set for the user portrait data of the reading user aiming at the user portrait data of the reading user, so that the actual requirements of the reading user are better met; in addition, the mode of recommending books related to the golden sentences to the user in a golden sentence list page mode can prevent the user from being in aesthetic fatigue, and the recommending mode can achieve thousands of people and make the user feel new.
Description
Technical Field
The invention relates to the field of computers, in particular to a recommendation method of an electronic book, electronic equipment and a computer storage medium.
Background
At present, with the improvement of reading consciousness, the number of users of electronic books is more and more, and in order to facilitate the users to select books suitable for the users, a plurality of electronic book platforms provide electronic book recommendation functions. Generally, a special book city page is provided in the e-book platform, and a user can browse related introductions of popular books by clicking the book city page, so as to select a suitable book through the book city page.
However, in the process of implementing the present invention, the inventor finds that the above solution in the prior art has at least the following defects: on one hand, due to the fact that the number of books on the page of the book city is limited, the problem that a user cannot select favorite books on the page of the book city frequently occurs; on the other hand, the electronic book of the page of the book city has a single presentation form, is fixed as a presentation form of a cover and a content introduction, and is easy to cause aesthetic fatigue of users due to a uniform presentation mode.
Disclosure of Invention
In view of the above problems, the present invention has been made to provide a recommendation method for an electronic book, an electronic device, and a computer storage medium that overcome or at least partially solve the above problems.
According to an aspect of the present invention, there is provided a recommendation method of an electronic book, including:
acquiring user portrait data of a reading user;
screening a golden sentence set matched with the reading user from a preset golden sentence database according to the user portrait data of the reading user and a preset golden sentence screening rule;
and displaying the screened golden sentence set in a preset golden sentence list page so as to recommend the electronic book corresponding to the golden sentence set to the reading user through the golden sentence list page.
According to another aspect of the present invention, there is provided an electronic apparatus including: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is configured to store at least one executable instruction that causes the processor to:
acquiring user portrait data of a reading user;
screening a golden sentence set matched with the reading user from a preset golden sentence database according to the user portrait data of the reading user and a preset golden sentence screening rule;
and displaying the screened golden sentence set in a preset golden sentence list page so as to recommend the electronic book corresponding to the golden sentence set to the reading user through the golden sentence list page.
According to yet another aspect of the present invention, there is provided a computer storage medium having at least one executable instruction stored therein, the executable instruction causing the processor to:
acquiring user portrait data of a reading user;
screening a golden sentence set matched with the reading user from a preset golden sentence database according to the user portrait data of the reading user and a preset golden sentence screening rule;
and displaying the screened golden sentence set in a preset golden sentence list page so as to recommend the electronic book corresponding to the golden sentence set to the reading user through the golden sentence list page.
According to the recommendation method of the electronic book, the electronic equipment and the computer storage medium, the golden sentence set matched with the current reading user can be screened from the preset golden sentence database according to the user portrait data of the reading user and the preset golden sentence screening rule, and the screened golden sentence set is displayed in the preset golden sentence list page, so that the electronic book corresponding to the golden sentence set is recommended to the reading user through the golden sentence list page. Therefore, the method can recommend the personalized gold sentence set for the user portrait data of the reading user aiming at the user portrait data of the reading user, so that the actual requirements of the reading user are better met; in addition, the mode of recommending books related to the golden sentences to the user in a golden sentence list page mode can prevent the user from being in aesthetic fatigue, and the recommending mode can achieve thousands of people and make the user feel new.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a flowchart illustrating a recommendation method for an electronic book according to an embodiment of the present invention;
fig. 2 is a flowchart illustrating a recommendation method for an electronic book according to another embodiment of the present invention;
fig. 3 shows a schematic structural diagram of an electronic device according to another embodiment of the invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
Example one
Fig. 1 shows a flowchart of a recommendation method for an electronic book according to an embodiment of the present invention. The "recommendation" in the present invention may also be understood as "push", and the present invention does not limit the specific implementation manner of the recommendation. As shown in fig. 1, the method comprises the steps of:
step S110: user portrait data of a reading user is acquired.
Wherein, reading the user portrait data of the user means: the present invention is not limited to the specific meaning of the user portrait data, and is applicable to data for describing user characteristics such as user behavior and user preference of the reading user. The invention has the following effects of acquiring the user portrait data: the book matching with the interest preference of the user can be recommended to the current user conveniently, so that personalized recommendation is achieved, and accordingly all feature data which are helpful for describing the interest preference of the user can be used as user portrait data.
Step S120: and screening a golden sentence set matched with the reading user from a preset golden sentence database according to the user portrait data of the reading user and a preset golden sentence screening rule.
The golden sentence database is used for storing golden sentences extracted from the electronic books in advance. By golden sentence, it is meant a sentence that is more classic, popular, and/or elegant in language. The golden sentence database can be constructed in various ways, can be extracted according to the reading behavior of a user, and can also be extracted manually by editing.
And the preset golden sentence screening rule is used for screening a golden sentence set matched with the user portrait data of the reading user from the golden sentence database, so that the screened golden sentence set is more in line with the personalized requirements of the reading user. The specific screening rules can be flexibly configured by those skilled in the art, and the present invention is not limited thereto.
Step S130: and displaying the screened golden sentence sets in a preset golden sentence list page so as to recommend the electronic book corresponding to the golden sentence sets to a reading user through the golden sentence list page.
The preset golden sentence list page is used for displaying the screened golden sentence sets, and when a reading user browses the golden sentence list page, the golden sentence list page is often attracted by golden sentences contained in the golden sentence sets, so that the corresponding electronic book is recommended to the user through the golden sentences. During specific implementation, the clause and relevant information such as the book title of the electronic book corresponding to the clause can be directly displayed in an associated manner, or only the clause can be displayed, and a hyperlink or other click entry is set for the clause, so that the electronic book information corresponding to the click operation executed by the user for the click entry of the clause is displayed after the click operation is received. The electronic book is recommended in a gold sentence mode, so that the defect caused by the insufficient number of books contained in the page of the book city can be effectively overcome. In addition, the method can enable the user to read a large number of famous and good sentences in the process of selecting the electronic book, so that the user's sentiment is mastered, and the user's identification capability is improved.
Therefore, the method can recommend the personalized gold sentence set for the user portrait data of the reading user aiming at the user portrait data of the reading user, so that the actual requirements of the reading user are better met; in addition, the mode of recommending books related to the golden sentences to the user in a golden sentence list page mode can prevent the user from being in aesthetic fatigue, and the recommending mode can achieve thousands of people and make the user feel new.
Example two
Fig. 2 is a flowchart illustrating a recommendation method for an electronic book according to another embodiment of the present invention. As shown in fig. 2, the method comprises the steps of:
step S210: user portrait data of a reading user is acquired.
In this embodiment, the user portrait data includes: reading behavior data, bookshelf recording information, and/or user preference information. The reading behavior data is used for reflecting information such as the number of books browsed by a user, book names, browsing time and the like. The bookshelf recording information comprises all book information of the user who added the book, and the favorite and the purchase record of the user can be reflected through the bookshelf recording information. The user preference information is used for reflecting the preference of the user to the preset field, for example, the user with preference for literature and the user with preference for science popularization, and accordingly, different contents can be recommended for the users with different preferences.
Step S220: and screening a golden sentence set matched with the reading user from a preset golden sentence database according to the user portrait data of the reading user and a preset golden sentence screening rule.
Wherein, the preset golden sentence screening rule comprises: screening a golden sentence set matched with reading behavior data and/or user preference information contained in user portrait data so as to screen books in which a user is interested; and/or screening a golden sentence set which is not matched with bookshelf recording information contained in the user portrait data so as to remove books purchased or browsed by the user.
In addition, in this embodiment, the preset clause filtering rule may further include at least one of the following:
(1) and when the candidate golden sentences correspond to the same electronic book, screening a golden sentence adding golden sentence set according to the appearance sequence of the candidate golden sentences in the electronic book.
Specifically, since the main purpose of the present invention is to recommend an electronic book corresponding to a gold sentence to a user through the gold sentence, in order to avoid recommending a duplicate electronic book to the user, the gold sentences belonging to the same electronic book need to be filtered to ensure that only one gold sentence in the same book is added to the gold sentence set. For example, only the first clause included in the same book may be added to the set of clauses, or the clauses in the highlights chapter or the important chapter may be added to the set of clauses.
(2) Filtering the clauses matched with a preset filtering list from the clause database and then screening; wherein the filtered list includes: the electronic books are configured in the book city list, screened within the last N days, and/or matched with the bookshelf recording information contained in the user portrait data; wherein N is a natural number.
Specifically, in order to prevent the user from being recommended books that have already been browsed, a preset filtering list needs to be configured. For example, the filtered list includes the electronic books configured in the book city list, and since the user has browsed the book city list, the electronic books configured in the book city list need to be added to the filtered list in order to avoid duplication with the existing content in the book city list. For another example, the filtered list includes electronic books that have been filtered within the previous N days, and since the user generally does not want to browse the same content for multiple times in a short period, the filtered list may be added to the electronic books that have been filtered within the previous N days, that is, the electronic books that have been added to the golden sentence set and recommended to the user within the previous N days, and when N is 3, the scheme may ensure that no duplicate content is recommended to the user through the golden sentence list page within three consecutive days. For another example, the filtering list includes an electronic book that matches bookshelf recording information included in the user portrait data, and the bookshelf recording information included in the user portrait data is used to record a book that the user has purchased, so that the method can ensure that the content existing in the bookshelf is not recommended to the user.
Therefore, the preset filtering list can be realized by a general filtering sublist which is generally used for each user in the whole network and a special filtering sublist which is specific to the current reading user. The general filtering sub-list is used for storing configured electronic books in the city book list, and the electronic books in the city book list may contain a large number of popular books which are generally used for the users in the whole network, so that the contents of the part of the contents are the same for the users in the whole network, and the parts of the contents can be stored through the general filtering sub-list. The special filtering sub-list is a plurality of sub-lists respectively corresponding to the reading users and is used for storing books browsed by the users in the previous N days through the golden sentence list pages and electronic books matched with bookshelf recording information contained in user image data of the users. By maintaining a special filtering sublist corresponding to each user and updating the special filtering sublist after showing the clause list page each time, the content in the clause list page can be ensured not to contain books purchased or browsed by the user, thereby improving the user experience and the recommendation success rate.
(3) And screening the electronic books under the preset classification according to the classification information and/or the downloading amount of the electronic books corresponding to the clauses.
Specifically, when the electronic books under the preset classification are screened according to the classification information of the electronic books corresponding to the clauses, the classification matching with the user preference information of the reading user may be obtained first, and the books under the classification matching with the user preference information may be preferentially recommended to the user. Specifically, recommendation is performed according to user behaviors, and relevant books in existing book categories in the bookshelf of the user are recommended to the user. For example, when reading a book that a user likes historical books and literature books, the book classification of the bookshelf includes a historical classification and a literature classification, and accordingly, the books under the historical classification and the literature classification can be preferentially screened.
In addition, in order to facilitate expanding the user preference to dig out more interests of the user, the electronic books under a plurality of preset classifications can be recommended to the user. For example, a plurality of preset classifications are preset, including: science fiction, suspicion, swordsmen and the like, correspondingly, an electronic book corresponding to each preset classification is screened for each preset classification so as to expand the interest of the user. In the specific screening, the screening may be performed according to the downloading amount, for example, the books with the highest downloading amount in each category are screened, so as to provide the hot-sold books in the category to the user. By the method, books under multiple classifications can be recommended to the user, the existing classifications in the bookshelf of the user are not limited, and therefore other classifications which are interesting to the user can be conveniently mined according to the conversion rate of the recommended books in the subsequent process. The classification information and the download amount information of the electronic book may be used in combination or may be used separately, which is not limited in the present invention.
As a result, according to the above rule, hot-sold books (books with the largest download amount) can be preferentially recommended to the user. In addition, in this embodiment, assuming that the total number of categories is M, one electronic book may be selected from each of the M categories, and the electronic books in each category are alternately displayed. For example, assuming that the total number of categories is 5, one e-book is selected from each category, and the obtained 5 e-books are alternately displayed to the user.
Step S230: and displaying the screened golden sentence set in a preset golden sentence list page.
In one implementation of this embodiment, each gold in the gold listing page includes: semantic content of the gold sentence and electronic book information corresponding to the gold sentence. For example, the semantic content of a gold sentence and the electronic book information corresponding to the gold sentence may be stored in an associated manner as a gold sentence data record, and accordingly, when being displayed, the semantic content of the gold sentence and the electronic book information corresponding to the gold sentence are displayed in an associated manner. In order to reduce the data size, the e-book information corresponding to the golden sentence may only include the book name information or the classification information, so as to give a prompt to the user, and the user can conveniently determine whether to browse the e-book corresponding to the golden sentence in detail.
In another implementation manner of this embodiment, each gold in the gold list page only includes semantic content of the gold, but does not include electronic book information corresponding to the gold, and if a user wishes to know detailed content of the gold, interactive operations such as clicking and the like need to be performed on the gold.
Step S240: when an interactive request triggered by a user aiming at the clauses in the clause list page is received, displaying the e-book page corresponding to the clauses in the clause list page so as to recommend the e-book contained in the e-book page to the user.
In order to facilitate the user to know the specific content of the electronic book corresponding to the clause, in the embodiment, an interactive element such as a hyperlink or a click entry is arranged for each clause in the clause list page, and when the user clicks the interactive element corresponding to the clause, an interactive request is triggered so as to jump to the electronic book page corresponding to the clause. The e-book page contains specific information of the e-book, such as the title, author, introduction, etc., so that the user can decide whether to purchase the e-book. In addition, in order to facilitate the user to purchase the electronic book, the electronic book page also comprises a purchase inlet, and the user can purchase the electronic book by one key by directly clicking the purchase inlet, so that the efficiency of purchasing the book is greatly improved.
In addition, in this embodiment, a plurality of clause list pages with different contents arranged in order may be configured in advance, and when a page turning instruction issued by a user is received, a next clause list page is displayed, so that the user can browse more clauses. Correspondingly, in this embodiment, the following presentation rule may be further configured: after the multiple screens are continuously displayed, the user is prompted in a mode of popping a frame and the like to 'no golden sentence today, please early tomorrow'. For example, the prompt may be made after 5 screens are presented consecutively, and the specific number is subject to the operation capability of the golden sentence library.
In summary, the personalized gold sentence list page can be recommended to the user by the method in the embodiment, so that the effect of thousands of people is achieved. In addition, the method recommends the books in a form of a gold sentence list page, and can give a user a new feeling. In addition, the gold sentence list page provided by the method can interact with the user, and the electronic book is recommended according to the interaction request of the user, so that the interest and the interactivity are good, and the user experience can be improved. In addition, in this embodiment, the golden sentences contained in the popular books under each category may be screened out and displayed in a preset golden sentence list page, that is: and a clause is screened in each classification, so that the user preference can be expanded, the user preference which is not captured before is captured, and a more appropriate book is recommended to the current user in the subsequent process.
In addition, the clauses in the clause database in this embodiment can be acquired in various ways. For example, a gold sentence may be manually selected by an editor and stored in a gold sentence database, or for example, may be obtained in a preset automatic obtaining manner. A method for automatically obtaining sentences to expand a database of sentences is provided below, the method comprising the steps of:
the method comprises the following steps: and acquiring user interaction data generated by each reading user aiming at the electronic book.
The data type of the user interaction data in this embodiment includes at least one of the following: annotation type, idea type, and/or comment type. The annotation type of the user interaction data can be realized by adding an underline to the text content or setting a highlighted annotation to the text content, and the specific annotation is not limited in the present invention.
Specifically, a piece of user interaction data may include the following information items: the data type of the user interaction data, the generation time of the user interaction data, the reading user identifier corresponding to the user interaction data, the electronic book identifier corresponding to the user interaction data, the text content corresponding to the user interaction data, the specific position information of the text content in the electronic book, and the like. In specific implementation, the user interaction data can be determined according to the user log information, and the acquisition source of the user interaction data is not limited by the invention.
Step two: and determining a sentence corresponding to the user interaction data contained in the electronic book as an interactive sentence.
Specifically, a piece of user interaction data is generated for one or more sentences, and accordingly, the sentences corresponding to the respective pieces of user interaction data are determined as interaction sentences, respectively. For example, assuming that a user adds an annotation to a sentence a, the sentence a is a sentence corresponding to the annotation-type user interaction data, and thus the sentence a is determined as an interactive sentence; for another example, assuming that the user adds an idea to statement B, statement B is a statement corresponding to the user interaction data of the idea type. Therefore, the determination mode of the interactive statement depends on the type of the user interactive data, and all statements corresponding to the user interactive data belong to the interactive statement. In addition, since the data type of the user interaction data includes a plurality of types, accordingly, the type of the interaction statement may be further divided into a plurality of types, for example, an annotation type of interaction statement, an idea type of interaction statement, and/or a comment type of interaction statement.
Step three: and calculating interaction values corresponding to the interaction statements according to the user interaction data.
Specifically, the interaction value corresponding to the interaction statement is used for reflecting the interaction depth between the user and the statement, and the interaction depth is related to various factors such as the number of interactions, the number of the interactive users, the type of interactions, and the like. For example, for an interactive statement, the greater the number of interactions between a user and the statement, the deeper the depth of interaction, the greater the value of interaction. In addition, in addition to the number of interactions, the number of interactive users may be further considered, for example, it is assumed that two interactive statements correspond to 10 interactions, but 10 interactions of a first statement are all completed by the same user, and 10 interactions of a second statement are completed by a plurality of different users, at this time, considering that the popularity of the book can be indirectly reflected by the interaction of the plurality of different users, so a larger interaction value may be set for the second statement. In addition, the interaction type can also be used to reflect the interaction depth, for example, for an interactive statement, the interaction depth for performing a marking class marking operation is less than the interaction depth for performing a thinking class operation, since the thinking class operation requires the user to spend more time and effort to implement. Accordingly, different weight values may be set for user interactions of various data types, respectively, for example, the weight value of the annotation type is lowest, the weight value of the annotation type is second, and the weight value of the idea type and/or the comment type is highest.
Therefore, the interactive value corresponding to the interactive statement can be determined according to various factors such as the number of times of interaction, the number of the interactive users, the type of interaction and the like, and the specific determination mode is not limited by the invention. In the embodiment, for each interactive statement, user interactive data corresponding to the interactive statement is acquired; determining each data type contained in the user interaction data corresponding to the interaction statement and an interaction value corresponding to each data type; and calculating an interaction value corresponding to the interaction statement according to the interaction value corresponding to each data type and a preset weight value corresponding to each data type. Specifically, when the interaction value corresponding to each interactive statement is calculated according to the user interaction data, the number of interactions corresponding to each interactive statement may be calculated according to the user interaction data, and the interaction value corresponding to each interactive statement is determined according to the number of interactions. For example, for a sentence a, it is assumed that user interaction data corresponding to the sentence a includes 10 pieces, where 3 pieces are mark types and 7 pieces are comment types, and if a weight value of a mark type is 0.3 and a weight value of a comment type is 0.6, an interaction value of the interactive sentence is 3 × 0.3+7 × 0.6 — 5.1.
In addition, the inventor finds that, in the process of implementing the present invention, since the reading completion rate of the electronic book is usually not up to a hundred percent, and most readers start reading from the beginning of the electronic book, the browsing times (i.e., exposure times) of users corresponding to the chapters close to the beginning are large, and the browsing times of users corresponding to the chapters close to the end are small, so that interactive sentences are intensively distributed in the chapters close to the beginning, and the browsing times of the chapters close to the end are low, so that the interactive sentences are few. In order to solve the above problem, in the present embodiment, when determining the interaction value corresponding to each interactive sentence according to the number of interactions, the influence of the number of exposures is considered at the same time instead of simply calculating according to the number of interactions of each interactive sentence, so as to pursue a fairer result. Specifically, for each interactive statement, the number of times of exposure of the interactive statement is determined, and an interaction value of the interactive statement is calculated according to the number of times of interaction of the interactive statement and a comparison result between the number of times of exposure of the interactive statement. The comparison result between the interactive times of the interactive statements and the exposure times of the interactive statements can be obtained in the form of the ratio between the interactive times of the interactive statements and the exposure times of the interactive statements, the ratio between the interactive times of the interactive statements and the exposure times of the interactive statements can reflect the conversion rate of the statements, and the higher the conversion rate is, the larger the interaction value is. The method can solve the problem that interactive sentences are difficult to mine due to low exposure times of the following chapters, so that the difference caused by chapter sequence is reduced, and more excellent sentences are extracted. In addition, in other embodiments of the present invention, the interaction value may be further calculated by combining the occurrence position and/or the occurrence frequency of the interactive sentence, for example, the occurrence position is represented by the sequence of the chapters, and the interaction value of the interactive sentence next to the chapter is greater than the interaction value of the interactive sentence next to the chapter when other factors are the same. For another example, under the condition that other factors are the same, the interactive value of the interactive sentence with the large number of occurrences is greater than the interactive value of the interactive sentence with the small number of occurrences, so that the classical sentence which is broadcast as the song is conveniently extracted.
Step four: and extracting the interactive sentences of which the interactive values accord with the preset extraction rules into golden sentences, and storing the extracted golden sentences into a preset golden sentence database.
Since the number of interactive sentences in a book is usually large, optionally, in this embodiment, the interactive sentences are further screened to select the interactive sentences with high quality as golden sentences. Specifically, the preset extraction rule may be multiple, and may be a single rule or multiple rules, and the specific content of the extraction rule is not limited in the present invention. In an implementation manner of this embodiment, interactive statements with an interactive value greater than a preset interactive threshold are extracted as golden statements.
In addition, the inventor finds that in the process of implementing the invention, in poetry and other types of literature works, two adjacent sentences or even multiple sentences are used together for expressing complete meanings. For example, when a classical paragraph is cited in an article, two sentences constituting the paragraph are closely related and inseparable, and at this time, if the two sentences are determined as two interactive sentences directly according to periods and whether each interactive sentence can be extracted as a gold sentence is separately determined, semantic splitting may be caused. In order to solve the above problem, in the present application, the clause is further extracted by the following preset extraction rule: for the extracted golden sentence, if an interactive sentence adjacent to the extracted golden sentence exists, further judging whether an interactive value corresponding to the interactive sentence adjacent to the extracted golden sentence is larger than a preset continuous threshold value; if yes, determining the interactive sentences adjacent to the extracted golden sentences and the extracted golden sentences as a continuous golden sentence. Under normal conditions, interactive sentences appearing at adjacent positions of the golden sentences are likely to be related sentences which are closely related and inseparable with the golden sentences, so that the related golden sentences which are closely related and inseparable can be accurately screened out by judging the interactive values of the adjacent sentences, the related golden sentences are determined as a continuous golden sentence together, and the continuous golden sentences are displayed as a whole in the subsequent process so as to avoid the situation of semantic splitting. The size of the preset continuous threshold can be flexibly set by a person skilled in the art, and can be the same as the preset interaction threshold or smaller than the preset interaction threshold. For example, when the preset continuous threshold is the same as the preset interactive threshold, the extracted golden sentence and the adjacent interactive sentences are both extracted as golden sentences, and at this time, only two extracted adjacent golden sentences need to be determined as a continuous golden sentence. For another example, when the preset continuous threshold is smaller than the preset interactive threshold, the adjacent interactive sentences corresponding to the extracted golden sentences may not be extracted as golden sentences, and at this time, the adjacent interactive sentences not extracted as golden sentences and the extracted golden sentences are determined as a continuous golden sentence. For example, when the extracted gold sentence and the adjacent sentence before (or after) the gold sentence are all performed by more than 80% of users to perform the marking operation such as line drawing, it indicates that the extracted gold sentence and the adjacent sentence before (or after) the gold sentence should be regarded as a continuous gold sentence. Therefore, the continuous gold sentence comprises at least two sentences, and the at least two sentences are labeled by a large number of users at the same time due to the close semantic relation.
The method can quickly and accurately extract the sentences which are more interesting to the users in the whole network based on the reading behaviors of the users, so that the finally determined sentences can better meet the requirements of the users in the whole network. Moreover, manual operation is not needed in the whole process, golden sentences can be automatically generated according to the reading behaviors of users in the whole network, and the efficiency is improved. The method can preferentially determine the sentences with intensive user interaction behaviors as the golden sentences, thereby conforming to the preference of the majority of users. In addition, the problem that golden sentences are concentrated at the front part of the book due to low reading completion rate can be solved. In addition, the situation that adjacent sentences with close semantic relation are divided into two golden sentences can be avoided by defining the continuous golden sentences. In a word, the method can provide and display the golden sentences for the current user by utilizing the interactive operation of the historical user. In addition, the method can be widely applied to different electronic book platforms so as to generate the golden sentences loved by platform users in the current platform. The inventor discovers that in the process of implementing the invention: because there is a certain difference between different e-book platforms, for example, the user groups of the a platform and the B platform are different, and the user preferences are different, if the clauses generated in the a platform are directly transplanted to the B platform, the clauses of the a platform are likely to cause an unpopular problem in the B platform. By the method, when the method is applied to the current electronic book platform, the user interaction data are also acquired by the full-network users in the current electronic book platform, so that the user preference of the current electronic book platform can be better reflected, the clauses suitable for the current electronic book platform can be quickly generated in the current electronic book platform, and the influence of platform difference is well solved.
EXAMPLE III
The embodiment of the application provides a non-volatile computer storage medium, wherein the computer storage medium stores at least one executable instruction, and the computer executable instruction can execute the recommendation method of the electronic book in any method embodiment.
The executable instructions may be specifically configured to cause the processor to:
acquiring user portrait data of a reading user;
screening a golden sentence set matched with the reading user from a preset golden sentence database according to the user portrait data of the reading user and a preset golden sentence screening rule;
and displaying the screened golden sentence set in a preset golden sentence list page so as to recommend the electronic book corresponding to the golden sentence set to the reading user through the golden sentence list page.
In an alternative form, the user representation data includes: reading behavior data, bookshelf recording information and/or user preference information;
the preset clause screening rule includes: screening a golden sentence set matched with reading behavior data and/or user preference information contained in the user portrait data; and/or screening a golden sentence set which is not matched with bookshelf recording information contained in the user portrait data.
In an optional manner, the preset clause filtering rule further includes at least one of the following:
when a plurality of candidate golden sentences correspond to the same electronic book, screening one golden sentence according to the appearance sequence of the candidate golden sentences in the electronic book and adding the golden sentence into the golden sentence set;
filtering the clauses matched with a preset filtering list from the clause database and then screening; wherein the filtered list comprises: the electronic books are configured in the book city list, screened within the last N days, and/or matched with the bookshelf recording information contained in the user portrait data; wherein N is a natural number; and
and screening the electronic books under the preset classification according to the classification information and/or the downloading amount of the electronic books corresponding to the clauses.
In an optional manner, the recommending, to the reading user through the gold listing page, the electronic book corresponding to the gold set includes:
and when an interactive request triggered by a user aiming at the clauses in the clause list page is received, displaying the e-book page corresponding to the clauses in the clause list page so as to recommend the e-book contained in the e-book page to the user.
In an alternative manner, each gold sentence in the gold sentence list page includes: semantic content of the gold sentence and electronic book information corresponding to the gold sentence.
In an alternative mode, the preset golden sentence database is created by:
acquiring user interaction data generated by each reading user aiming at the electronic book;
determining sentences contained in the electronic book and corresponding to the user interaction data as interaction sentences;
calculating an interactive value corresponding to each interactive statement according to the user interactive data, and extracting the interactive statements of which the interactive values accord with a preset extraction rule into golden sentences;
and storing the extracted golden sentences into the preset golden sentence database.
In an optional manner, the preset extraction rule includes: and extracting the interactive sentences of which the interactive values are greater than the preset interactive threshold value into golden sentences.
In an optional manner, the preset extraction rule includes:
for the extracted golden sentence, if an interactive sentence adjacent to the extracted golden sentence exists, further judging whether an interactive value corresponding to the interactive sentence adjacent to the extracted golden sentence is larger than a preset continuous threshold value;
and if so, determining the interactive sentence adjacent to the extracted golden sentence and the extracted golden sentence as a continuous golden sentence.
Example four
Fig. 3 is a schematic structural diagram of an electronic device according to another embodiment of the present invention, and the specific embodiment of the present invention does not limit the specific implementation of the electronic device.
As shown in fig. 3, the electronic device may include: a processor (processor)302, a communication Interface 304, a memory 306, and a communication bus 308.
Wherein: the processor 302, communication interface 304, and memory 306 communicate with each other via a communication bus 308. A communication interface 304 for communicating with network elements of other devices, such as clients or other servers. The processor 302 is configured to execute the program 310, and may specifically execute relevant steps in the above recommendation method for an electronic book.
In particular, program 310 may include program code comprising computer operating instructions.
The processor 302 may be a central processing unit CPU, or an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement an embodiment of the present invention. The electronic device comprises one or more processors, which can be the same type of processor, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs.
And a memory 306 for storing a program 310. Memory 306 may comprise high-speed RAM memory and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The program 310 may specifically be configured to cause the processor 302 to perform the following operations:
acquiring user portrait data of a reading user;
screening a golden sentence set matched with the reading user from a preset golden sentence database according to the user portrait data of the reading user and a preset golden sentence screening rule;
and displaying the screened golden sentence set in a preset golden sentence list page so as to recommend the electronic book corresponding to the golden sentence set to the reading user through the golden sentence list page.
In an alternative form, the user representation data includes: reading behavior data, bookshelf recording information and/or user preference information;
the preset clause screening rule includes: screening a golden sentence set matched with reading behavior data and/or user preference information contained in the user portrait data; and/or screening a golden sentence set which is not matched with bookshelf recording information contained in the user portrait data.
In an optional manner, the preset clause filtering rule further includes at least one of the following:
when a plurality of candidate golden sentences correspond to the same electronic book, screening one golden sentence according to the appearance sequence of the candidate golden sentences in the electronic book and adding the golden sentence into the golden sentence set;
filtering the clauses matched with a preset filtering list from the clause database and then screening; wherein the filtered list comprises: the electronic books are configured in the book city list, screened within the last N days, and/or matched with the bookshelf recording information contained in the user portrait data; wherein N is a natural number; and
and screening the electronic books under the preset classification according to the classification information and/or the downloading amount of the electronic books corresponding to the clauses.
In an optional manner, the recommending, to the reading user through the gold listing page, the electronic book corresponding to the gold set includes:
and when an interactive request triggered by a user aiming at the clauses in the clause list page is received, displaying the e-book page corresponding to the clauses in the clause list page so as to recommend the e-book contained in the e-book page to the user.
In an alternative manner, each gold sentence in the gold sentence list page includes: semantic content of the gold sentence and electronic book information corresponding to the gold sentence.
In an alternative mode, the preset golden sentence database is created by:
acquiring user interaction data generated by each reading user aiming at the electronic book;
determining sentences contained in the electronic book and corresponding to the user interaction data as interaction sentences;
calculating an interactive value corresponding to each interactive statement according to the user interactive data, and extracting the interactive statements of which the interactive values accord with a preset extraction rule into golden sentences;
and storing the extracted golden sentences into the preset golden sentence database.
In an optional manner, the preset extraction rule includes: and extracting the interactive sentences of which the interactive values are greater than the preset interactive threshold value into golden sentences.
In an optional manner, the preset extraction rule includes:
for the extracted golden sentence, if an interactive sentence adjacent to the extracted golden sentence exists, further judging whether an interactive value corresponding to the interactive sentence adjacent to the extracted golden sentence is larger than a preset continuous threshold value;
and if so, determining the interactive sentence adjacent to the extracted golden sentence and the extracted golden sentence as a continuous golden sentence.
The algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names.
Claims (21)
1. A recommendation method of an electronic book comprises the following steps:
acquiring user portrait data of a reading user;
screening a golden sentence set matched with the reading user from a preset golden sentence database according to the user portrait data of the reading user and a preset golden sentence screening rule;
displaying the screened golden sentence set in a preset golden sentence list page so as to recommend an electronic book corresponding to the golden sentence set to the reading user through the golden sentence list page;
the preset golden sentence database is created in the following way: acquiring user interaction data generated by each reading user aiming at the electronic book; determining sentences contained in the electronic book and corresponding to the user interaction data as interaction sentences; calculating an interactive value corresponding to each interactive statement according to the user interactive data, and extracting the interactive statements of which the interactive values accord with a preset extraction rule into golden sentences; storing the extracted golden sentences in the preset golden sentence database; calculating the interaction times corresponding to the interactive statements according to the user interaction data, and determining the interaction values corresponding to the interactive statements according to the interaction times; determining the exposure times of the interactive statements aiming at each interactive statement, and calculating the interactive value of the interactive statement according to the interactive times of the interactive statements and the ratio of the exposure times of the interactive statements; and the ratio of the interactive times of the interactive statement to the exposure times of the interactive statement is used for reflecting the conversion rate of the statement, and the higher the conversion rate is, the larger the interactive value is.
2. The method of claim 1, wherein the user representation data comprises: reading behavior data, bookshelf recording information and/or user preference information;
the preset clause screening rule includes: screening a golden sentence set matched with reading behavior data and/or user preference information contained in the user portrait data; and/or screening a golden sentence set which is not matched with bookshelf recording information contained in the user portrait data.
3. The method of claim 1, wherein the preset clause filtering rule further comprises at least one of:
when a plurality of candidate golden sentences correspond to the same electronic book, screening one golden sentence according to the appearance sequence of the candidate golden sentences in the electronic book and adding the golden sentence into the golden sentence set;
filtering the clauses matched with a preset filtering list from the clause database and then screening; wherein the filtered list comprises: the electronic books are configured in the book city list, screened within the last N days, and/or matched with the bookshelf recording information contained in the user portrait data; wherein N is a natural number; and
and screening the electronic books under the preset classification according to the classification information and/or the downloading amount of the electronic books corresponding to the clauses.
4. The method of any of claims 1-3, wherein the recommending, to the reading user via the gold listing page, the e-book corresponding to the set of gold sentences comprises:
and when an interactive request triggered by a user aiming at the clauses in the clause list page is received, displaying the e-book page corresponding to the clauses in the clause list page so as to recommend the e-book contained in the e-book page to the user.
5. The method of any of claims 1-3, wherein each gold in the gold listing page comprises: semantic content of the gold sentence and electronic book information corresponding to the gold sentence.
6. The method of claim 1, wherein the preset extraction rule comprises: and extracting the interactive sentences of which the interactive values are greater than the preset interactive threshold value into golden sentences.
7. The method of claim 1, wherein the preset extraction rule comprises:
for the extracted golden sentence, if an interactive sentence adjacent to the extracted golden sentence exists, further judging whether an interactive value corresponding to the interactive sentence adjacent to the extracted golden sentence is larger than a preset continuous threshold value;
and if so, determining the interactive sentence adjacent to the extracted golden sentence and the extracted golden sentence as a continuous golden sentence.
8. An electronic device, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is configured to store at least one executable instruction that causes the processor to:
acquiring user portrait data of a reading user;
screening a golden sentence set matched with the reading user from a preset golden sentence database according to the user portrait data of the reading user and a preset golden sentence screening rule;
displaying the screened golden sentence set in a preset golden sentence list page so as to recommend an electronic book corresponding to the golden sentence set to the reading user through the golden sentence list page;
the preset golden sentence database is created in the following way: acquiring user interaction data generated by each reading user aiming at the electronic book; determining sentences contained in the electronic book and corresponding to the user interaction data as interaction sentences; calculating an interactive value corresponding to each interactive statement according to the user interactive data, and extracting the interactive statements of which the interactive values accord with a preset extraction rule into golden sentences; storing the extracted golden sentences in the preset golden sentence database; calculating the interaction times corresponding to the interactive statements according to the user interaction data, and determining the interaction values corresponding to the interactive statements according to the interaction times; determining the exposure times of the interactive statements aiming at each interactive statement, and calculating the interactive value of the interactive statement according to the interactive times of the interactive statements and the ratio of the exposure times of the interactive statements; and the ratio of the interactive times of the interactive statement to the exposure times of the interactive statement is used for reflecting the conversion rate of the statement, and the higher the conversion rate is, the larger the interactive value is.
9. The electronic device of claim 8, wherein the user representation data comprises: reading behavior data, bookshelf recording information and/or user preference information;
the preset clause screening rule includes: screening a golden sentence set matched with reading behavior data and/or user preference information contained in the user portrait data; and/or screening a golden sentence set which is not matched with bookshelf recording information contained in the user portrait data.
10. The electronic device of claim 8, wherein the preset clause filtering rule further comprises at least one of:
when a plurality of candidate golden sentences correspond to the same electronic book, screening one golden sentence according to the appearance sequence of the candidate golden sentences in the electronic book and adding the golden sentence into the golden sentence set;
filtering the clauses matched with a preset filtering list from the clause database and then screening; wherein the filtered list comprises: the electronic books are configured in the book city list, screened within the last N days, and/or matched with the bookshelf recording information contained in the user portrait data; wherein N is a natural number; and
and screening the electronic books under the preset classification according to the classification information and/or the downloading amount of the electronic books corresponding to the clauses.
11. The electronic device of any of claims 8-10, wherein the executable instructions specifically cause the processor to:
and when an interactive request triggered by a user aiming at the clauses in the clause list page is received, displaying the e-book page corresponding to the clauses in the clause list page so as to recommend the e-book contained in the e-book page to the user.
12. The electronic device of any of claims 8-10, wherein each gold in the gold listing page comprises: semantic content of the gold sentence and electronic book information corresponding to the gold sentence.
13. The electronic device of claim 8, wherein the preset extraction rule comprises: and extracting the interactive sentences of which the interactive values are greater than the preset interactive threshold value into golden sentences.
14. The electronic device of claim 8, wherein the preset extraction rule comprises:
for the extracted golden sentence, if an interactive sentence adjacent to the extracted golden sentence exists, further judging whether an interactive value corresponding to the interactive sentence adjacent to the extracted golden sentence is larger than a preset continuous threshold value;
and if so, determining the interactive sentence adjacent to the extracted golden sentence and the extracted golden sentence as a continuous golden sentence.
15. A computer storage medium having at least one executable instruction stored therein, the executable instruction causing a processor to:
acquiring user portrait data of a reading user;
screening a golden sentence set matched with the reading user from a preset golden sentence database according to the user portrait data of the reading user and a preset golden sentence screening rule;
displaying the screened golden sentence set in a preset golden sentence list page so as to recommend an electronic book corresponding to the golden sentence set to the reading user through the golden sentence list page;
the preset golden sentence database is created in the following way: acquiring user interaction data generated by each reading user aiming at the electronic book; determining sentences contained in the electronic book and corresponding to the user interaction data as interaction sentences; calculating an interactive value corresponding to each interactive statement according to the user interactive data, and extracting the interactive statements of which the interactive values accord with a preset extraction rule into golden sentences; storing the extracted golden sentences in the preset golden sentence database; calculating the interaction times corresponding to the interactive statements according to the user interaction data, and determining the interaction values corresponding to the interactive statements according to the interaction times; determining the exposure times of the interactive statements aiming at each interactive statement, and calculating the interactive value of the interactive statement according to the interactive times of the interactive statements and the ratio of the exposure times of the interactive statements; and the ratio of the interactive times of the interactive statement to the exposure times of the interactive statement is used for reflecting the conversion rate of the statement, and the higher the conversion rate is, the larger the interactive value is.
16. The computer storage medium of claim 15, wherein the user representation data comprises: reading behavior data, bookshelf recording information and/or user preference information;
the preset clause screening rule includes: screening a golden sentence set matched with reading behavior data and/or user preference information contained in the user portrait data; and/or screening a golden sentence set which is not matched with bookshelf recording information contained in the user portrait data.
17. The computer storage medium of claim 15, wherein the preset clause filtering rule further comprises at least one of:
when a plurality of candidate golden sentences correspond to the same electronic book, screening one golden sentence according to the appearance sequence of the candidate golden sentences in the electronic book and adding the golden sentence into the golden sentence set;
filtering the clauses matched with a preset filtering list from the clause database and then screening; wherein the filtered list comprises: the electronic books are configured in the book city list, screened within the last N days, and/or matched with the bookshelf recording information contained in the user portrait data; wherein N is a natural number; and
and screening the electronic books under the preset classification according to the classification information and/or the downloading amount of the electronic books corresponding to the clauses.
18. The computer storage medium of any of claims 15-17, wherein the executable instructions specifically cause the processor to:
and when an interactive request triggered by a user aiming at the clauses in the clause list page is received, displaying the e-book page corresponding to the clauses in the clause list page so as to recommend the e-book contained in the e-book page to the user.
19. The computer storage medium of any of claims 15-17, wherein each gold in the gold listing page comprises: semantic content of the gold sentence and electronic book information corresponding to the gold sentence.
20. The computer storage medium of claim 15, wherein the preset extraction rule comprises: and extracting the interactive sentences of which the interactive values are greater than the preset interactive threshold value into golden sentences.
21. The computer storage medium of claim 15, wherein the preset extraction rule comprises:
for the extracted golden sentence, if an interactive sentence adjacent to the extracted golden sentence exists, further judging whether an interactive value corresponding to the interactive sentence adjacent to the extracted golden sentence is larger than a preset continuous threshold value;
and if so, determining the interactive sentence adjacent to the extracted golden sentence and the extracted golden sentence as a continuous golden sentence.
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| CN111651209B (en) * | 2019-03-04 | 2024-04-16 | 阿里巴巴集团控股有限公司 | Method and device for determining guide page |
| CN110532309B (en) * | 2019-07-15 | 2022-05-03 | 浙江工业大学 | Generation method of college library user portrait system |
| CN110597980B (en) * | 2019-09-12 | 2021-04-30 | 腾讯科技（深圳）有限公司 | Data processing method and device and computer readable storage medium |
| CN110647637B (en) * | 2019-09-25 | 2020-10-30 | 掌阅科技股份有限公司 | Electronic book-based associated content display method and electronic equipment |
| CN110851710A (en) * | 2019-10-28 | 2020-02-28 | 上海连尚网络科技有限公司 | Novel recommendation method and device |
| CN110968789B (en) * | 2019-12-04 | 2023-05-23 | 掌阅科技股份有限公司 | Electronic book pushing method, electronic equipment and computer storage medium |
| CN111125543B (en) * | 2019-12-17 | 2023-03-10 | 掌阅科技股份有限公司 | Training method of book recommendation sequencing model, computing device and storage medium |
| CN111782933B (en) * | 2020-04-10 | 2024-06-14 | 北京沃东天骏信息技术有限公司 | Method and device for recommending booklets |
| CN111782946A (en) * | 2020-06-30 | 2020-10-16 | 掌阅科技股份有限公司 | Book friend recommendation method, calculation device and computer storage medium |
| CN112307173A (en) * | 2020-11-18 | 2021-02-02 | 托普爱英(北京)科技有限公司 | Learning assisting method and device and electronic equipment |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10013462B2 (en) * | 2015-07-28 | 2018-07-03 | Microsoft Technology Licensing, Llc | Virtual tiles for service content recommendation |
Family Cites Families (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040114258A1 (en) * | 2002-12-17 | 2004-06-17 | Harris Richard Alexander | Device and method for combining dynamic mathematical expressions and other multimedia objects within a document |
| CN101373486B (en) * | 2008-10-16 | 2010-06-02 | 北京航空航天大学 | Personalized summary system based on user interest model |
| CN103914550B (en) * | 2014-04-11 | 2017-08-18 | 百度在线网络技术（北京）有限公司 | Show the method and apparatus of content recommendation |
| CN106611050B (en) * | 2016-12-22 | 2018-08-03 | 掌阅科技股份有限公司 | E-book recommends method, apparatus and server |
| CN107562951A (en) * | 2017-09-28 | 2018-01-09 | 咪咕数字传媒有限公司 | A kind of information displaying method, device and computer-readable recording medium |
| CN108108465A (en) * | 2017-12-29 | 2018-06-01 | 北京奇宝科技有限公司 | A kind of method and apparatus for pushing recommendation |
| CN108520076B (en) * | 2018-04-19 | 2020-11-10 | 掌阅科技股份有限公司 | Electronic book recommendation method, electronic device and computer storage medium |
-
2018
- 2018-11-21 CN CN201811389030.0A patent/CN109271594B/en active Active
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10013462B2 (en) * | 2015-07-28 | 2018-07-03 | Microsoft Technology Licensing, Llc | Virtual tiles for service content recommendation |
Non-Patent Citations (2)
| Title |
|---|
| The Advantage and Module Study for Introducing E-Books in Book Design Course;Jiawen Hao;《IEEE Xplore》;20160613;第671-674页 * |
| 个性化推荐网上书城的研究与实现;张佳丽;《中国优秀硕士学位论文全文数据库》;20151215;第I138-264页 * |
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